文章基本信息

标题：PATRONES DE SUPERVIVENCIA PARA LA GESTIÓN DE LOS CENTROS ESPECIALES DE EMPLEO
本地全文：下载
作者：Vera Gelashvili ; María Jesús Segovia Vargas ; María del Mar Camacho Miñano 等
期刊名称：Revista de Estudios Empresariales. Segunda Época
印刷版ISSN：0213-8964
电子版ISSN：1988-9046
出版年度：2015
期号：1
语种：Spanish
出版社：Universidad de Jaén
摘要：Los Centros Especiales de Empleo (CEEs) son empresas rentables que contratan a trabajadores con discapacidad para su normalización en la sociedad. Debido a su crecimiento, competitividad en el mercado de trabajo y a la labor que están desempeñando en el marco de la economía social, estas empresas han suscitado interés recientemente. Sin embargo, los CEEs se enfrentan a un gran desafío y es que deben ser competitivos en el mercado para garantizar su supervivencia. El objetivo de este estudio es analizar la supervivencia de los CEEs españoles, determinando cuáles serían las variables clave que condicionan su continuidad en el mercado, o por el contrario, su fracaso empresarial. La muestra inicial es la totalidad de los CEEs de España, 1.668 empresas. Se recogió la información de sus estados financieros durante el último año disponible. A continuación se clasificaron los CEEs en tres grupos (sanos, dudosos y con elevadas probabilidades de insolvencia) en función de la puntación obtenida al utilizar el Z- score de Altman. Después, se elaboró un modelo de Inteligencia Artificial (algoritmo C4.5) para modelizar las características básicas de los CEEs de los tres grupos anteriormente mencionados. La principal contribución de este estudio es que con los ratios de deudas sobre ventas netas, la rentabilidad económica y el test ácido podemos predecir qué CEEs sobrevivirán y cuáles tendrán más dificultades para mantenerse en el mercado. Además, la metodología IA utilizada aporta un enfoque distinto al tradicional usando técnicas estadísticas. ABSTRACT Sheltered Employment Centers (CEEs) are profitable firms that contract workers with disability to prepare them for incorporation in society. Due to their growth, competitiveness in the labor market and work in the framework of the social economy, these companies have attracted some interest recently. However, these enterprises have to face great challenges because they have to be competitive in the market in order to guarantee their survival. The objective of this paper is to analyze the survival of the Sheltered Employment Centers, to ascertain the key variables that condition their continuity in the market, or otherwise, their business failure. The initial sample is the total number of CEEs in Spain, 1.668 firms. The financial statements of all were extracted from 2013, the last period available. Then, the all CEEs were classified in tree groups (healthy, doubtful and distressed), according to their Altman Z’-score. A method of artificial intelligence (algorithm C 4.5) was used in order to obtain the basic patterns of each of the groups. The main contribution of this study is that we can know which CEEs survive with the ratios of debts over net sales, return on assets, and quick ratio and which one will have more difficulties to stay in the market. Moreover, the artificial intelligence methodology used is a new approach compared to traditional statistical techniques.
其他摘要：Sheltered Employment Centers (CEEs) are profitable firms that contract workers with disability to prepare them for incorporation in society. Due to their growth, competitiveness in the labor market and work in the framework of the social economy, these companies have attracted some interest recently. However, these enterprises have to face great challenges because they have to be competitive in the market in order to guarantee their survival. The objective of this paper is to analyze the survival of the Sheltered Employment Centers, to ascertain the key variables that condition their continuity in the market, or otherwise, their business failure. The initial sample is the total number of CEEs in Spain, 1.668 firms. The financial statements of all were extracted from 2013, the last period available. Then, the all CEEs were classified in tree groups (healthy, doubtful and distressed), according to their Altman Z’-score. A method of artificial intelligence (algorithm C 4.5) was used in order to obtain the basic patterns of each of the groups. The main contribution of this study is that we can know which CEEs survive with the ratios of debts over net sales, return on assets, and quick ratio and which one will have more difficulties to stay in the market. Moreover, the artificial intelligence methodology used is a new approach compared to traditional statistical techniques.